Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/dragoon0x/usemindit/mindit-confidencenpx skills add Dragoon0x/usemindit --skill mindit-confidencegit clone --depth 1 https://github.com/Dragoon0x/useminditWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dragoon0x/usemindit/mindit-confidence)<a href="https://agentmods.dev/skills/dragoon0x/usemindit/mindit-confidence"><img src="https://agentmods.dev/badge/skills/dragoon0x/usemindit/mindit-confidence.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00105 | $0.01236 |
| Opus 5 | $0.00053 | $0.00618 |
| Sonnet 5 | $0.00021 | $0.00247 |
| Haiku 4.5 | $0.00011 | $0.00124 |
Grade A, and why
mindit-confidence scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mindit-confidence
The seventh of the eight forces. Run this when the question is "how do we actually know."
The force
Every design decision is backed by something. Sometimes that something is real research — interviews, observations, data, prior experiments. Sometimes it is convention — "this is how everyone does it." Sometimes it is intuition — "I think users would want this." Sometimes it is assumption — "we believe users will."
These are not equal. Confidence is highest with triangulated, fresh, broad, well-mechanized evidence. Confidence is lowest with single-source, stale, narrow, weakly-mechanized claims, or with no claims at all.
Confidence asks: where on this spectrum does this decision actually live?
The goal is not to require evidence for every decision. The goal is to know which decisions you are flying blind on, so you can decide whether to invest in research or move forward on conviction.
When to run this
- The user states a belief about users ("users want X," "users will not Y," "users prefer Z").
- The user mentions research, interviews, A/B tests, surveys, analytics, or "what the data says."
- The user has just shipped or is about to ship a hypothesis-driven design.
- The user uses phrases like "we know," "we believe," "I think," "users tend to."
How to analyze
-
Surface the claims. Walk through the design and extract every claim about users it implicitly or explicitly makes. ("Users want fewer steps," "users skim before reading," "users will trust this badge.")
-
For each claim, ask three questions:
- What evidence backs this? Research, data, convention, intuition, assumption.
- How fresh is the evidence? When was it produced? Has the user base changed since?
- How broad is the evidence? One user, ten, thousands? One segment or all segments?
-
Score the criteria below. Each criterion measures one dimension of evidence quality.
-
Distinguish "thin evidence" from "no evidence." Both are valid findings but they get different fixes. Thin evidence can be strengthened. No evidence requires deciding whether to invest in research or proceed on conviction.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 89 lines · 105 tokens per session scan A 66518fe9a81f
mindit-confidence is a skill published in the GitHub repository Dragoon0x/usemindit (2 stars, last pushed 3mo ago), licensed MIT. It adds 105 tokens to every session and 1,236 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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